accuracy not vetted

---
title: "geoCat"
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    vertical_layout: scroll
    social: menu
    source_code: embed
---

# accuracy not vetted

Use these results without warranty or express or implied fitness for any purpose.  The results are not vetted for accuracy.  The results are not vetted for completeness.  The results are not vetted for usability. So says siri


```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE)


library(data.table)
library(highcharter)
library(dplyr)
library(knitr)
library(maps)
data("county.fips")
data("uscountygeojson")
data("usgeojson")

#the COUNTYFP column is a character column, so we don't want to convert it to numeric
# df <- fread("../output/output_county_count.csv_combined.csv", colClasses = c("COUNTYFP" = "character"))
df <- fread("../output/output_state_count.csv_combined.csv", colClasses = c("COUNTYFP" = "character"))
test=df[which(df$STATE_NAME=="Wisconsin"),]
# order the data frame by the counts column
test= test[order(-test$counts),]

```

Column {data-width=650}
-----------------------------------------------------------------------


```{r echo=FALSE, message=FALSE, warning=FALSE}


# function to sum the counts column by a given column name
sum_by_column <- function(in_df, column_names) {
  #group by the list of column names and sum the counts column
  in_df <- in_df[, .(counts = sum(counts)), by = column_names]
  return(in_df)
}

```

### Count of Tracks by State
```{r}
sumByState <-
  sum_by_column(in_df = df, column_names = c("STATE_NAME"))
# log10 transform the counts column
sumByState$counts <- log10(sumByState$counts)
#create a chlorealpleth map of the US
highchart() %>%
  hc_add_series_map(
    usgeojson,
    sumByState,
    value = "counts",
    joinBy = c("name", "STATE_NAME"),
    name = "Count",
    dataLabels = list(enabled = TRUE, format = '{point.name}')
  ) %>%
  hc_mapNavigation(enabled = TRUE) %>%
  hc_colorAxis(stops = color_stops()) %>%
  hc_title(text = "Count of Tracks by State") %>%
  hc_add_theme(hc_theme_smpl()) %>%
  hc_legend(enabled = FALSE) %>%
  hc_tooltip(pointFormat = "{point.name}: {point.value}")

```

### Count of Tracks by County

polygons may not capture water like wisconsin apostle islands


```{r}
# TODO where is California counties
df$COUNTY_FIP=paste0(df$STATEFP,df$COUNTYFP)
sumByCounty <-
  sum_by_column(in_df = df, column_names = c("COUNTY_FIP"))
# log10 transform the counts column
sumByCounty$counts <- log10(sumByCounty$counts)
#create a chlorealpleth map of the US
highchart() %>%
  hc_add_series_map(
    uscountygeojson,
    sumByCounty,
    value = "counts",
    joinBy = c("fips", "COUNTY_FIP"),
    name = "Count") %>%
  hc_mapNavigation(enabled = TRUE) %>%
  hc_colorAxis(stops = color_stops()) %>%
  hc_title(text = "Count of Tracks by County") %>%
  hc_add_theme(hc_theme_smpl()) %>%
  hc_legend(enabled = FALSE) 
# %>%
#   hc_tooltip(pointFormat = "{point.name}: {point.value}")

```

### cat counts

```{r}
# map the Names "Rye8" "RyePhone" and "Rye13" to "RyeCat"
# create a vector of the names that need to be mapped
namesToMap <- c("Rye8", "RyePhone", "Rye13")
# replace the names in the Name column with "RyeCat"
# table(namesToMap %in% df$Name)
df$Name[df$Name %in% namesToMap] <- "RyeCat"

# get the names that start with sofia or that contain Papa or that start with tonga
tongaNames <- df$Name[grepl("^tonga", df$Name, ignore.case = TRUE)]
papaNames <- df$Name[grepl("papa", df$Name, ignore.case = TRUE)]
sofiaNames <- df$Name[grepl("^sofia", df$Name, ignore.case = TRUE)]
iacatNames <- c(tongaNames, papaNames, sofiaNames,"iha")
# map the iacatNames to iacat
df$Name[df$Name %in% iacatNames] <- "iacat"

# print the unique cat names that are not RyeCat or iacat
# unique(df$Name[!(df$Name %in% c("RyeCat", "iacat"))])

# subset the data frame to only include the rows where the Name column is equal to "RyeCat" or "iacat"
df <- df[Name %in% c("RyeCat", "iacat"), ]
 
# count wich cat wins which state
# the column Name contains the name of the cat
# the column counts contains the number of tracks
# create a color for the Name column with the most counts in each state
dfOriginal <- df
# get the max count for each state
maxByState <- df[, .(counts = max(counts)), by = c("STATE_NAME")]
# merge the max count back to the original data frame
df <- merge(df, maxByState, by = c("STATE_NAME"))
# filter the data frame to only include the rows where the counts column is equal to the max count
df <- df[counts.x == counts.y, ]
# create a color column that is equal to the name column
df$color <- df$Name
# create a color palette that maps RyeCat to bl
colorPalette <- c("#0000FF","#FF0000")
# create a named vector of colors
colorVector <- setNames(colorPalette, unique(df$Name))
# replace the color column with the color vector
df$color <- colorVector[df$color]
# log10 transform the counts column
df$counts <- log10(df$counts.x)
#create a chlorealpleth map of the US
highchart() %>%
  hc_add_series_map(
    usgeojson,
    df,
    value = "counts",
    joinBy = c("name", "STATE_NAME"),
    name = "Count",
    dataLabels = list(enabled = TRUE, format = '{point.name}')
  ) %>%
  hc_mapNavigation(enabled = TRUE) %>%
  hc_colorAxis(stops = color_stops()) %>%
  hc_title(text = "Which Cat Wins Which State") %>%
  hc_add_theme(hc_theme_smpl()) %>%
  hc_legend(enabled = FALSE) %>%
  hc_tooltip(pointFormat = "{point.name} is won by {point.Name}: {point.value}")

```

### cat counts

```{r}
# bar chart of number of states won by each cat
# count the number of states won by each cat
countByCat <- df[, .(counts = .N), by = c("Name")]
# create a bar chart of the number of states won by each cat
highchart() %>%
  hc_add_series(
    countByCat,
    type = "bar",
    hcaes(x = Name, y = counts),
    name = "Count"
  ) %>%
  hc_title(text = "Number of States Won by Each Cat") %>%
  hc_add_theme(hc_theme_smpl()) %>%
  hc_legend(enabled = FALSE) %>%
  hc_tooltip(pointFormat = "{point.Name} won {point.counts} states")